Artificial intelligence system for optimizing informational content presentation

ABSTRACT

At an artificial intelligence system, a baseline set of informational content elements pertaining to an item for presentation to one or more potential item consumers is identified. One or more optimization iterations are implemented. In a particular iteration, a data set comprising interaction records of a target audience with the baseline set and with one or more variants of the baseline set is collected. Using the data set as input to a machine learning model, effectiveness metrics of the different informational elements are determined. A particular content element set to be presented to an audience is identified using the effectiveness metrics.

BACKGROUND

Many content providers, such as e-retail organizations or librarieswhich sell, lend or stream content items such as books, periodicals,motion pictures and the like, may have large inventories comprisingmillions of items. In order to attract consumers for the content items,a number of interfaces may be used to present collateral informationabout the items—e.g., excerpts from reviews may be presented, images ofbook covers or authors may be presented, and so on. Furthermore, suchinformational content may be presented in a number of differentcontexts—e.g., as part of a recommendation, in response to a searchrequest, in an advertisement, and so on.

For many inventory items, numerous versions may be available from agiven e-retailer or similar organization. For example, in the case ofbooks, multiple editions published over the years may be available; formotion pictures, DVD and Blu-Ray editions may be available,released-to-theater and director's cuts may be available, and so on. Insome cases, multiple different versions of the same underlying logicalcollateral elements may be available for presentation—e.g., differenteditions of a book may have respective covers, sets of critics' reviews,customer reviews, and so on. The different versions may vary in theirability to attract potential consumers of the corresponding contentitems—e.g., depending on the type of book and its intended audience, abook cover with a vividly colored, visually striking combination ofimages and text may be much more successful at increasing sales than adull single-colored cover.

In today's competitive environment, increasing the probability that agiven potential customer for a given content item actually purchases orconsumes the item can, when aggregated over millions of items andmillions of customers, have a significant impact on the organizationproviding the items. Selecting the appropriate set of informationalcontent to increase such probabilities may present a non-trivialtechnical challenge.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example system environment in whichmachine-learning based techniques for optimizing the presentation ofinformational content for items of an inventory may be implemented,according to at least some embodiments.

FIG. 2 illustrates examples of informational content elements pertainingto a book, according to at least some embodiments.

FIG. 3 illustrates example categories of informational content elements,according to at least some embodiments.

FIG. 4 illustrates example sources of informational content elements,according to at least some embodiments.

FIG. 5 illustrates aspects of an example high-level workflow foroptimizing presentation of informational content elements, according toat least some embodiments.

FIG. 6 illustrates example presentation contexts for informationalcontent elements, according to at least some embodiments.

FIG. 7 illustrates example effectiveness metrics for presentedinformational content elements, according to at least some embodiments.

FIG. 8 illustrates example granularities at which the presentation ofinformational content elements may be optimized, according to at leastsome embodiments.

FIG. 9 illustrates example programmatic interactions between aninformational content optimization service and its clients, according toat least some embodiments.

FIG. 10 illustrates a provider network environment at which aninformational content optimization service may be implemented, accordingto at least some embodiments.

FIG. 11 is a flow diagram illustrating aspects of operations that may beperformed to optimize the presentation of informational contentelements, according to at least some embodiments.

FIG. 12 is a block diagram illustrating an example computing device thatmay be used in at least some embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to. When used in the claims,the term “or” is used as an inclusive or and not as an exclusive or. Forexample, the phrase “at least one of x, y, or z” means any one of x, y,and z, as well as any combination thereof.

DETAILED DESCRIPTION

Various embodiments of methods and apparatus for optimizing theeffectiveness of the presentation of informational content associatedwith inventory items are described. The term “informational contentelement” or ICE associated with an inventory item may be used in variousembodiments to refer to an artifact, such as an image, a video, a fewphrases or a paragraph of text, etc. which in some way describes theitem or conveys some information pertaining to the item, in a way thatmay be useful to potential consumers of the item in deciding whetherthey wish to consume (e.g., purchase or lease) the item. In at leastsome embodiments, one or more of the techniques described herein may beemployed at a large-scale content provider such as an e-retailer whichan inventory comprising millions of items, where for at least some ofitems, numerous choices for ICEs such as different versions of images ofvarious aspects of the items (e.g., different cover images forrespective editions of a book), text sequences such as reviews, newsitems pertaining to items (such as rewards or reward nominations forbooks, films, etc.) and so on may be available. Furthermore, for atleast some of the items, multiple presentation contexts may be usable todisplay or indicate the ICEs in various embodiments—e.g., one or morerecommendation-related interfaces may be available, various types ofsearch result interfaces may be implemented, an overview or item summaryweb page may be generated for each item, a details page may begenerated, and so on. In the e-retail environment, the variouspresentation contexts may, for example, be encountered during anavigation of a web site which can potentially conclude with thepurchase (or borrowing/leasing) of the items.

At a high level, the content presentation optimization methodology maybe summarized as follows for at least some embodiments. For a given itemof an inventory, a baseline set of one or more informational contentelements may first be identified for presentation in at least somepresentation context. A number of different techniques may be used toidentify the baseline set in different embodiments—e.g., a canonical ormost-recently-generated set of ICEs may be used, a machine learningmodel may be trained to predict an effective combination of ICE featuresusing records of earlier consumer interactions with ICEs, a randomcollection of ICEs may be selected from among the available ICEs, and soon. After the baseline set has been identified, one or more optimizationiterations may be conducted in some embodiments using a machine learningmodel operating in an online or continuous learning mode. In effect, thebaseline set may serve as a “prior” with respect to which iterativeexperiments involving tradeoffs between exploration and rewardexploitation may be performed in various embodiments. In a giveniteration one or more variants of the (current) baseline set may bepresented to targeted audiences, and the effectiveness or utility ofdifferent ICEs may be learned based on the responses of the targetaudiences. Using the learned effectiveness measures, the set of ICEs tobe presented to one or more potential consumers of the item may beadjusted, in effect resulting in the identification of a new baselinefor the next optimization iteration in various embodiments. Theoptimization iterations may be continued in some embodiments, with theeffectiveness of additional ICE variants being estimated in eachiteration, until some termination criterion is met. The terminationcriteria may, for example, include determining that the marginalimprovements in effectiveness being achieved by the variants hasplateaued, that the overall consumption rate of the set of items forwhich ICE presentation is being optimized has fallen below somethreshold which renders further optimizations inessential, and so on. Ineffect, the benefits of presenting various individual versions orvariants of ICEs may be learned dynamically in an ongoing process,enabling the provider of the inventory to adjust to potentially changingtrends in consumer behavior in at least some embodiments.

The exploration of the presentation space and the associatedoptimization may be performed in some embodiments using so-called“bandit” machine learning models and algorithms, such as a contextualbandit algorithm or other similar state-dependent adaptive trialalgorithms. In a contextual bandit algorithm which may be employed inone embodiment, a multi-dimensional feature vector called a contextvector may be generated from the available ICE choices. When making aparticular choice for ICE recommendations in a given optimizationiteration, the context vector and the rewards/losses corresponding topast choices may be analyzed, with the tradeoffs between exploration(the extent of changes made to the previously-presented sets of ICEs)and exploitation (maximization of rewards) being taken into account insuch an embodiment. Over time, the bandit algorithm may examine enoughinformation obtained from the ongoing collection of records ofinteractions with the ICEs to learn relationships among the rewards andthe context vector elements, and may therefore be able to select moreeffective ICEs quickly and efficiently in various embodiments. Otheroptimization algorithms, including for example neural network-basedreinforcement learning algorithms/models, may be employed in differentembodiments.

According to some embodiments, a system may comprise one or morecomputing devices of a network-accessible artificial intelligenceservice, e.g., a service implemented at a provider network or cloudcomputing environment. A set of records pertaining to previous sales orconsumption of various items of an inventory (e.g., via web-basedinterfaces or other types of network access) over some period of time(e.g., months or years) may be available for analysis by the service inat least some embodiments. Individual ones of the records may indicate,for example, the kinds of ICEs which were presented corresponding tovarious sales or item consumption events (consumption events which maynot necessarily comprise sales may comprise, for example,subscription-based streaming of content, borrowing of items, and so on).The computing devices may train, using such records of earlierinteractions, a first machine learning model to generate respectiveeffectiveness scores of different ICE features (e.g., characteristicssuch as color content, text font and layout etc. of different book coverimages) in one or more presentation contexts (e.g., a recommendationcontext, an item overview web page, etc.). In some embodiments, thefirst machine learning model may also or instead be trained to generaterespective effectiveness scores for ICE categories (e.g., images versusreviews) and/or individual instances of ICEs. Using a trained version ofthe first machine learning model, a baseline set of informationalcontent elements pertaining to a particular item may be identified in atleast some embodiments. The baseline set may be identified forpresentation to one or more potential item consumers in a particularpresentation context, and may for example comprise a first version of afirst ICE (e.g., a particular image of a book's cover) selected fromamong a plurality of versions of the first ICE.

In at least one embodiment, starting with the baseline set of ICEsidentified using the first machine learning model, one or morepresentation optimization iterations may be performed. In a first suchpresentation optimization iteration, a new data set comprising recordsof interactions, in at least one presentation context, of a targetaudience with (a) the baseline set and (b) one or more variants of thebaseline set may be obtained. A particular variant may, for example,comprise a second version of the first informational content element.The new data set may be provided as input to a second machine learningmodel operating in an online or continuous learning mode, from whichrespective effectiveness metrics corresponding to various ICEs includedin the variants may be determined. Using the output of the second model,adjustments may be made to the set of ICEs presented to various targetaudience members for the next optimization iteration in someembodiments.

In at least some embodiments, one or more of the ICEs presented topotential item consumers may be synthesized or created de novo—that is,a content generator tool such as a generative neural network-basedmachine learning model may be trained to generate ICEs (such as images)which are likely to be more effective in attracting item consumers thanthe available set of ICEs for a given item. In at least one embodiment,a synthetic ICE generator tool may be employed, when, for example,insufficient ICEs of a particular type are available from the originalproducers or vendor of the corresponding item. In some embodiments, atraining data set for a synthetic content generation tool may comprise aprovided list of features of an item I and example ICEs (e.g., for otheritems) with desirable characteristics.

In one embodiment, as mentioned earlier, the initial baseline set ofICEs may be selected without utilizing a machine learning model. Forexample, a default rule for selecting among various versions of an ICEsuch as a book cover image may indicate that the most recent versionshould be used in some embodiments. In another example, random selectionfrom among the population of available ICEs may be used to populate aninitial baseline set. In some embodiments in which machine learningmodels are used for baseline ICE set identification as well as variantexploration, optimal or near-optimal combinations of ICEs may at leastin some cases be identified more quickly than if the baselines wereidentified without using machine learning—that is, the first phase ofmachine learning may “boost” the second phase by allowing optimizationiterations to begin nearer to a true optimum. In one embodiment, machinelearning may be used for baseline ICE set identification, and theresults achieved using the baseline may be sufficient that no furtheroptimization iterations are performed—that is, the use of machinelearning may be confined to baseline ICE set identification in suchembodiments.

Respective sets of optimization iterations may be conducted in someembodiments for each of several presentation contexts for a given item.Examples of the different presentation contexts for ICEs may includerecommendation contexts, search result contexts, item overview contexts,item details contexts, advertisements using different types of messagingmechanisms such as e-mails, social media tools, flyers, newspapers andthe like. In some embodiments, a voice context may be used forpresenting at least some ICEs—e.g., review excerpts may be provided by avoice-driven assistant device or another Internet-of-Things (IoT)device. Categories of ICEs which may be presented in one or morecontexts may include, among others, still images, videos, audiorecordings, text collections, web links, and so on. In some embodimentsin which several different presentation contexts are available for agiven item, the different contexts may be prioritized relative to oneanother—e.g., if far more consumers purchased an item as a result ofviewing a recommendation than as a result of viewing an item detailspage, the optimization of the recommendation interface may beprioritized higher than the optimization of the details page. Records ofearlier interactions performed for similar items (or the same item) inthe different contexts may be analyzed to prioritize among contexts insuch embodiments.

In various embodiments, different types of effectiveness or utilitymetrics may be generated for ICEs at the machine learning models used(e.g., either during the iterative optimization phase, or during theidentification of the baseline sets of ICEs). Such metrics may include,for example, web link click count metrics, sales metrics, shopping cartinsertion metrics, wish list insertion metrics, and/or sessionengagement length metrics.

In one embodiment, any of a number of granularities corresponding torespective target audiences of item consumers may be selected foroptimizing the presentation of ICEs. The granularity levels may include,for example, global granularity (where all possible consumers areconsidered), group granularity (e.g., for potential consumers with someshared demographic or geographic characteristics), or individualgranularity (for a single individual). Depending on the selectedgranularity, the set of interaction records collected during theoptimization iterations, and the duration for which the interactions aremonitored or tracked for a given iteration, may change. For example, inthe case of a group granularity of approximately a hundred individualswith some common demographic property, it may take more time toaccumulate enough observations for N different variants of an ICE setthan if a global audience was targeted for optimization. Of course, asthe size of the group for which optimization is being attemptedincreases, the precision with which ICEs can be customized may decreaseas well in various embodiments.

Example System Environment

FIG. 1 illustrates an example system environment in whichmachine-learning based techniques for optimizing the presentation ofinformational content for items of an inventory may be implemented,according to at least some embodiments. As shown, system 100 comprisesvarious components of an informational content optimization service(ICOS) 120 which may be utilized by the owners/managers of anetwork-accessible item inventory 150 to attain various types ofobjectives associated with the inventory. The inventory 150 may comprisea large number of individual items 144 in some embodiments, for at leastsome subset of which collateral descriptive information 174 may beavailable. For example, a large online retailer may have an inventorycomprising millions of items 144, and the collateral information 174pertaining to a given item 144 may comprise a collection ofinformational content elements (ICEs) such as various versions orinstances of still or video images, reviews, and so on. The ICEs may bepresented (e.g., via visual interfaces such as web sites, audiointerfaces and the like) to potential and actual consumers 180 of theitems in various presentation contexts in the depicted embodiment, e.g.,in response to programmatic requests (such as search requests) submittedvia programmatic interfaces 177 or as part of advertisements/promotionalmessages targeted at consumers 180. The inventory owning organizationsmay keep track of the effectiveness of the presented ICEs, e.g., bylogging information about interactions of the consumers 180 with variouspresentation interfaces, as indicated by arrow 147. Depending on thenumber of different presentation contexts used, a number of repositoriesor sources 102 of the interaction records may be available for potentialanalysis by the ICOS, such as source 112A or source 112B.

The ICOS 120 may comprise a plurality of component entities in variousembodiments, individual ones of which may be implemented using acombination of software and hardware of one or more computing devices.For example, as shown, the service 120 may comprise some number ofanalysis workflow coordinators 133, machine learning models andresources 115, baseline ICE selectors 122, ICE variants explorers 124,presentation context prioritizers 126, and/or synthetic contentgenerators 121 in the depicted embodiment.

The overall process of optimizing the presentation of the ICEs forvarious items 144 may be orchestrated by one or more analysis workflowcoordinators 133 of the ICOS 120 in various embodiments. For example,such coordinators 133 may invoke or activate other components of theICOS to determine baseline sets of ICEs for various items, select orgenerate (using synthetic content generators 121) variants of ICEs to bepresented in an effort to learn about the effectiveness of differentICEs, and so on. In various embodiments, machine learning algorithms andresources 115 (e.g., computing devices which are optimized for machinelearning tasks) may be employed at one or more stages of theoptimization process, although the use of machine learning models maynot be required for at least some stages of the optimization.

In some embodiments, data sets comprising records obtained from theinteraction record sources 102 (as indicated by arrow 162) may be usedas input to train a machine learning model to identify a baseline set ofICEs to be presented to some subset or all of inventory consumers 180 inone or more contexts for various items 144. Such a model may be trainedto generate, corresponding to individual features of various availableICEs items 144, respective utility scores or presentation effectivenessscores for one or more presentation contexts in one embodiment. Thebaseline ICE selectors 122 may include the set of ICEs with featureswhose scores are highest, or whose scores exceed some threshold, in abaseline set for a given item and context. In other embodiments, thebaseline ICE selectors 122 may identify baseline ICEs for at least someitems without invoking a machine learning model—e.g., a set ofheuristics or rules (such as the equivalent of “select the most recentversion of an instance of each ICE category such as book cover images”)may be used in some embodiments, or the baseline set of ICEs may beselected using random selection from among the available ICEs for anitem.

Starting with a baseline set of ICEs for a context, an ICE variantsexplorer 124 may being optimization iterations in some embodiments. Forexample, in a given iteration, some number of modifications to a currentbaseline ICE set may be selected (or synthesized) for experimentation,and ICE sets representing such modifications may be presented torespective subsets of consumers 180 in one or more contexts. Theeffectiveness (as measured by various types of metrics such as salesrate, click-through rate on web links, add-to-shopping-cart rates, etc.)of various of ICEs may be determined, e.g., using a contextual banditalgorithm (or a similar optimization or machine learning model) in someembodiments. Based at least in part on the effectiveness resultsobtained for the different ICE variants tried, a recommendation may begenerated in some embodiments for the set of ICEs which are to form thebaseline for the next optimization iteration. In the next iteration,assuming that variants of the new baseline ICEs still remain to beexplored, the effectiveness analysis may be repeated. Feedback based onconsumer interactions with the different variants may be provided to themachine learning models being used for the optimization iterations insome embodiments. The models used for the optimization in suchembodiments may operate in an online mode, in which new observations areanalyzed and learned from as soon as they become available (as opposedfor example to models which operate in batch mode, where a discretelearning or training phase may be followed by the execution of the modelwithout continuous training/learning). A machine learning model used foriterative optimization may make tradeoffs between exploration (makingmore changes to the baseline, with potentially less predictable changesto effectiveness) and exploitation (maximizing the effectiveness of thepresentation of the ICEs) in the depicted embodiment, eventuallyconverging towards a set of recommended ICEs for a presentation contextas indicated by arrow 163.

As shown in FIG. 1, a set of inventory presentation recommendations 140generated using ICE variant exploration may indicate for example thatfor a particular item 144A, a first set of ICEs 158A should be used incontext 147, while another set of ICEs 158B should be used in context148. Similarly, for the combination (item 144B, presentation context147), ICEs 158C may be recommended, and for the combination (item 144B,presentation context 149), ICEs 158D may be recommended in the depictedexample scenario. It is noted that ICEs need not be recommended for thesame set of presentation contexts for all items in at least someembodiments—for example, no recommendations may be generated for thecontext 148 for item 144B, and no recommendations may be generated forthe context 149 for item 144A in FIG. 1. The relative importance ofdifferent presentation contexts may vary for different items in someembodiments; in at least some embodiments, some presentation contextsmay not even be supported or implemented for a subset of items. In theembodiment depicted in FIG. 1, one or more presentation contextprioritizers 126 may determine which particular presentation contextsshould be considered candidates for ICE optimization for various items,and/or for deciding the order in which content presentation for thosecontexts should be optimized. In some embodiments, interaction recordsobtained from sources 102 may be used to determine the relevance ofdifferent contexts—e.g., if the sources indicate that for a particularitem category, search results are far more frequently the path towardsitem purchases than recommendations, then the search resultspresentation context may be prioritized higher than recommendationcontexts.

In at least some embodiments, one or more ICEs may be created at theICOS for some items, e.g., instead of utilizing pre-existing ICEsprovided by the producers/suppliers of the items. In some embodiments,as discussed below, synthetic content generators 121 comprising deepneural networks (DNNs) may be used to create the new ICEs. SyntheticICEs may be included in baseline ICE sets and/or in the variantexploration phase in some embodiments.

In various embodiments, feedback resulting from the implementation ofrecommendations produced by the ICOS may be used to improve the accuracyof future recommendations (e.g., for subsequent iterations on behalf ofa given item 144, or for newly introduced items). As indicated by arrow164, interaction records corresponding to the recommendations 140 may beadded to the sources 102, and may be analyzed on an ongoing basis by theICOS components. In at least some embodiments, directives to initiateoptimization for one or more items 144 may be submitted programmaticallyto the service 120 by ICOS clients 181, e.g., using a set ofprogrammatic interfaces 177.

Informational Content Elements

In various embodiments, the inventory for which ICE presentationoptimization is performed may comprise books. FIG. 2 illustratesexamples of informational content elements pertaining to a book,according to at least some embodiments. The book 210 may, for example,be a classic for which many different editions or versions are availablein the inventory, even though the vast majority of the logical contentof the book may not have changed from edition to edition. In someembodiments, all the different editions/versions may have the sameunique “parent” item identifier within the databases used to manage theinventory comprising the book 210.

Corresponding to the different versions/editions of the book, respectivesets of ICEs 220 may be available for presentation. ICE set 220A maycomprise, for example, a first book cover image 222A, excerpts 223 fromprofessional reviews of the book, and/or reader feedback excerpts 224Ain the depicted embodiment, all pertaining to an edition E1 of the book210. Corresponding to another edition E2, a different book cover image222B and a different set of reader feedback excerpts 224B may beavailable as ICEs, together with a set of links 225 to television orfilm adaptations may be available as part of ICE set 220B. Finally, withrespect to a different edition E3, a third version 222C of the book'scover and a set of celebrity blurbs 226 may be available as ICE set220C.

Depending, for example, on the results of the analysis performed at anICOS for book 210, different ones of the ICEs shown in FIG. 2 may berecommended for inclusion in one or more presentation contexts. Forexample, for a recommendation carousel 250 (a web-based interface inwhich respective images associated with various recommended items areprovided and a potential consumer can move quickly back and forth amongthe images), book cover image 222B which contains bright colors, largeand a title displayed in a large and visually striking font, may bepredicted to be more effective than image 222A (with dull colors and asmall font) or image 222C (with only black and white colors and a mediumfont). For an item overview web page context 260, reader feedbackexcerpts 224B and celebrity blurbs 226 may be recommended or preferred,while for a details web page pertaining to the book 210, professionalreview excerpts 223 and links to TV/film adaptations 225 may bepreferred.

A wide variety of ICEs may be employed in some embodiments. FIG. 3illustrates example categories of informational content elements,according to at least some embodiments. As shown, some ICEs 310 maycomprise static images 312, such as book covers, CD/record covers in thecase of music items, film promotion posters, item photographs taken fromdifferent angles/viewpoints and so on in the depicted embodiment. OtherICEs may comprise portions or all of text descriptions or reviews 314,which may be generated for example by professional critics or reviewers,and/or by members of the public. In some embodiments, audio items 316,such as excerpts of music, songs, advertising jingles, podcasts, and thelike may be used as ICEs for some items.

In some embodiments, videos 318, e.g., trailers, video reviews and thelike may be used to convey information about some items of an inventory.Features derived from videos 318 and static images 312 may collectivelybe referred to as image-based features in various embodiments. Newsitems 320, such as awards granted to a given item, awards for which anitem has been nominated, sales rankings published by trusted parties,etc. may be used as ICEs in some embodiments. For items comprising text,such as books, excerpts 322 of the items themselves may be used as ICEsin various embodiments. In at least one embodiment, links to relatedobjects or entities 324, such as TV/film adaptations in the case ofbooks, video games and the like may also be considered ICEs.

The particular combination of ICE categories may vary for differentitems of an inventory in different embodiments—that is, not all theitems in a given inventory, or even with an item category, may haveassociated ICEs of the same categories. In at least some embodiments,one or more ICEs for an item may be generated or synthesized by theICOS, instead of being provided by the producer/vendor of the item.

FIG. 4 illustrates example sources of informational content elements,according to at least some embodiments. In the depicted embodiment, thepool of available ICEs 450 for a given item of an inventor may originatefrom three broad categories of sources. The producers, suppliers orowners 410 of an item may comprise a primary source of ICEs in someembodiments. For example, a company that manufactures an electronicsitem may provide various types of metadata for the item, including anumber of images showing the item from various angles or viewpoints, aswell as brief descriptions of the items, and such images anddescriptions may be presented as ICEs 450. Similarly, in the case ofbooks, CDs, records, films, television shows and the like, thepublishers of content items may provide various types of visual,text-based, audio or video information which can potentially be used asICEs.

For many types of items or products, information generated by trustablethird parties 420 may be very significant contributors to the success orfailure of the items in various embodiments. Professional and/or amateurreviewers and critics may voice their opinions, e.g., either directly ata set of web pages being used to sell an item, or via publications,blogs, and other opinion distribution mechanisms. Portions of suchopinions may be used (after obtaining the necessary permissions from theauthors, if required) as ICEs in various embodiments.

For some items, instead of or in addition to using ICEs generated by theitem's producers or trusted third parties, the ICOS may create ICEsusing synthetic content generation tools 430 in some embodiments. Thisoption may be exercised if, for example, very little collateralinformation is available about a given item, or if the available ICEsare evaluated as being inadequate based on selected criteria in variousembodiments. Such content generation tools, which may for exampleinclude generative neural network models, may take a representation ofthe item's features and/or one or more example ICEs for similar items asinput 470, learn the desired characteristics of ICEs such as images (oreven text descriptions) using one or more neural networks 475, andgenerate synthetic ICEs with the desired characteristics representingthe items in at least some embodiments. In some embodiments, new coverimages for books or other content may be created using such techniques,for example. In some embodiments, item features input to the generativemodel 470 may be extracted from the item itself—e.g., images within abook may be used as possible starting points for a new image for thecover, or text extracted from the item may be interpreted to obtain astarting image. In at least one embodiment, the effectiveness of thesynthesized content may be evaluated using a machine learning modelsimilar to the model used for baseline ICE set identification, and/orthe set of desirable features may be identified using such a baselineidentification model. In different embodiments, the extent to which thesynthetic content is original may differ—e.g., some new image ICEs maybe generated by transforming existing images (by cropping, rotation,color change or the like) and/or combining existing images, while othersmay be created from scratch. Synthetic ICEs comprising any desiredcombination of several types of content (e.g., images only, images+text,video only, video+text, audio only, audio+text) may be generated indifferent embodiments. In some embodiments, synthetic ICEs may begenerated during the online exploration phase of the analysis, e.g., inaddition to or instead of being generated prior to the identification ofthe baseline set of ICEs.

Optimization Workflow Overview

FIG. 5 illustrates aspects of an example high-level workflow foroptimizing presentation of informational content elements, according toat least some embodiments. In the depicted example, to simplify theexplanation, for a particular item J of an inventory, a small set 510 ofICEs is assumed to be available for presentation in a particularpresentation context. The available ICEs (some of which may have beengenerated by item suppliers/producers or third parties and provided tothe ICOS, while others may have been created at the ICOS itself usingcontent generation tools 533 similar to tools 420 of FIG. 4) maycomprise content elements of three categories A, B and C. For ICEcategory A, variants A1, A2 and A3 may be available. For ICE category B,variants B1 and B2 may be available, while for ICE category C, variantsC1 and C2 may be available.

In the depicted example, a baseline ICE set 520 {A1, B2, C1} comprisingone ICE of each of the three categories may have been identified foritem J and the presentation context being considered. A machine learningmodel 530 (such as a regression model, a decision tree-based model suchas a random forest model, and/or a deep learning model deployingconvolutional neural networks or the like), trained using records ofearlier interactions with items similar to J (or with J itself) toproduce effectiveness or utility scores for different ICE features orICE categories, may be used to identify the baseline set 520 in someembodiments. In some embodiments, such a machine learning model may betrained and/or run in batch or offline mode. In other embodiments,instead of or in addition to a machine learning model, a set ofheuristics or rules 531 may be used to select the baseline set 520. Insome embodiments, the optimization process with respect to a given itemmay be initiated by identifying a baseline set 520 at any of variousstages of the item's lifecycle within the inventory—for example, whenthe item is first introduced into the inventory, or after some period oftime during which one or more un-optimized ICEs associated with the itemmay have been presented to an audience of potential consumers.

A variant explorer machine learning model 525, running in online moderather than batch mode, may generate one or more variants 540 of thebaseline set in the depicted embodiment. For example, variant ICE set540A may comprise {A1, B1, C1}, while variant ICE set 540B may comprise{A2, B2, C1}. The effectiveness of the three combinations if ICEs—thebaseline set 520, variant 540A and variant 540B—may be evaluated bypresenting the combinations of a selected optimization target audience570 in the depicted embodiment. The respective effectiveness results545A, 545B and 546 of the two variants and the baseline set (based onthe interactions of the target audience 570 with the presented ICEs) maybe used by the variant explorer to select a recommended ICE set 550(e.g., the ICEs of variant 540B) which may represent a new baseline setfor the next iteration of variant exploration and optimization in someembodiments. In some embodiments, a bandit model which updates itsparameters based on ongoing results may be employed as the variantexplorer as discussed above. In other embodiments, a reinforcementlearning algorithm may be used. The process of iterative optimizationmay be continued, with new effectiveness results generated in eachiteration potentially being used to gradually improve the targetedobjectives of the inventory owner with respect to the item J. In atleast some embodiments, at least a subset of the records of theinteractions of the target audience 570 with the variants (and/or theeffectiveness results 545A, 545B or 546 derived from such records) mayserve as feedback signals to the content generation tools 533 aswell—that is, new ICE variants may be generated based at least in parton the results of the explorations, and such new ICE variants may beused for further exploration iterations.

The target audiences 570 to which the baseline set 520 and its variantsare exposed, and from which the effectiveness results are captured, mayvary in different embodiments, based on the granularity at whichoptimization is desired with respect to item J. The target audience may,for example, comprise potential item consumers of a selected demographicor geographic group, or a single potential consumer, in someembodiments. In one embodiment, all potential consumers may be targeted,instead of restricting the optimization to an individual or a group. Insome embodiments, the effectiveness results obtained by presenting ICEsin a particular context may be used to generate recommended ICEs forother contexts—e.g., if a particular version of a book cover image isfound to be successful at increasing sales in a recommendation context,that same version may be recommended for an item overview page contextas well. In at least one embodiment, if an optimized set of ICEs hasbeen identified for a given presentation context, some or all of theICEs of that optimized set may be used as part of the baseline set ofICEs for a different presentation context for which optimization is tobe initiated. It is noted that in embodiments in which machine learningmodels are used both for baseline ICE set identification and for variantexploration/optimization, different sets of training data may be usedfor the two models. The baseline selection model 530 may be trained, forexample, using a large set of records of earlier interactions for itemsthat have been in the inventory for some time, while the online modelvariant ML explorer model 525 may learn from interactions with arelatively newly-introduced item for which not many records of earlierinteractions are available. In some embodiments in which machinelearning models are used for baseline ICE set identification as well asvariant exploration, optimal or near-optimal combinations of ICEs may atleast in some cases be identified more quickly than if the baselineswere identified without using machine learning. In one embodiment,machine learning may be used for baseline ICE set identification, andthe results achieved using the baseline may be close enough to a desiredtarget level that no further optimization iterations may be required.

In various embodiments, implementations of each of the machine learningmodels used (e.g., to select the baseline set of ICEs, to explorevariants, and/or to generate new ICEs) may, for example, include memoryfor storing input values and parameters and computer-executableinstructions for an initial set of computations on the input values. Insome embodiments, intermediary layers of the model may include memorystoring computer-executable instructions and/or data for manipulatingthe results of the initial computations and determining values to betransmitted to an output layer. The output layer may in turn includememory and/or computer-executable instructions for generating and/orstoring output values such as effectiveness scores. Any of a number oftypes of data structures may be used for storing data and/orimplementing the algorithm logic, e.g., including various tree-basedstructures as well as data structures optimized for storing matrices,vectors, arrays, hash tables and the like.

Presentation Contexts

FIG. 6 illustrates example presentation contexts for informationalcontent elements, according to at least some embodiments. In someembodiments, contexts 610 may include graphical recommendationinterfaces 612, such as carousels comprising images of recommended itemsor lists of recommended items. Recommendation interfaces may includeimages, text and/or other forms of content in some embodiments.

Search results interfaces 614 may comprise another prominent mechanismfor presenting ICEs in at least some embodiments. A number of differenttypes of search tools may be used in different embodiments to submitqueries about items in an inventory—e.g., text-based search (with orwithout auto-fill), image-base search and/or voice-based search may besupported, and each such tool may have a corresponding results interfacefor which ICE presentation may be optimized.

In scenarios in which items belong to an inventory of an e-retail (ore-wholesale) web site, item summary/overview web pages 616 and itemdetails web pages 618 may represent additional opportunities forpresenting ICEs associated with a given item in some embodiments. In atleast one embodiment, a number of alternative layouts may be possiblefor arranging a set of ICEs within a given item overview web page 616 oritem details web page 618. For example, the relative positioning of twoimages, or of an image and a text extract, may potentially be modifiedin different layouts. In some embodiments, in addition to exploring theeffectiveness of different ICEs, the effectiveness of different layoutscomprising the same set of ICEs may also be explored by an ICOS, withrecommended layouts being identified as the output of a machine learningmodel.

In at least some embodiments, voice interaction interfaces 620 mayprovide another context in which ICEs are presented to potential itemconsumers. For example, a voice-drive assistant device may be used topresent vocalized information about items of an inventory in response tospecific queries and/or based on the assistant device owner'spreferences. In one embodiment, ICE optimization may be performed forone or more forms of advertisements 622, such as advertisements sent viae-mails, social media tools, newspapers/periodicals, flyers and thelike. In some embodiments, comparison tools 624, such as web pages whichallow potential consumers to compare features of different items, mayrepresent another presentation context for ICEs. ICE presentationoptimization may be performed for one or more context not shown in FIG.6 in some embodiments. In at least one embodiment, ICE presentationoptimization may not necessarily be implemented for one or more of thecontexts shown in FIG. 6.

Presentation Effectiveness Metrics

As mentioned earlier, in at least some embodiments, machine learningmodels may be used for identifying baseline sets of ICEs and/or forexploring variants from baseline sets. Such models may be trained toproduce effectiveness metrics (which may also be referred to as utilitymetrics) of various kinds for features of the ICEs being considered.FIG. 7 illustrates example effectiveness metrics for presentedinformational content elements, according to at least some embodiments.

In some embodiments, the total number of completed sales or orders 712over some time interval during which a particular set of ICEs waspresented may be used as an effectiveness metric. In one embodiment,instead of using the absolute number of sales, the ratio 714 of ordersor sales to presentations may be used as the metric.

In some embodiments, together with ICEs being presented, a link on whicha potential item consumer may click to obtain additional information orto purchase the item may be provided, and the click-through rate 716(the rate at which the link for a particular item was clicked on, or theratio of clicks to presentations of the link) may be used as aneffectiveness metric.

E-retail web sites may track how often a given item was inserted into ashopping cart (even if the item was not purchased shortly after such aninsertion), or how often an item was added to a public or private “wishlist” of a potential consumer. Such wish lists may be used to indicatethat a potential consumer would like to obtain the inserted item, butwill not necessarily purchase the inserted item immediately. Wish listinsertions may, for example, serve as a signal to potential gift-giversregarding the consumer's preferences, or may simply be used as atemporary repository for items the consumer may purchase later. The rate718 of insertion of an item into a wish list or a shopping cart maybeused as another effectiveness metric in some embodiments.

In some cases, one or more items of an inventory may not necessarilyhave to be purchased, but may be consumed in other ways—e.g., digitizedbooks may be borrowed rather than bought, or films/videos may bestreamed by a subscriber rather than purchased. In some embodiments,depending on the item consumption model, data stream initiations orcompletions 720 may be used as effectiveness metrics.

In one embodiment, the owner of the inventory may measure the lengths ofthe sessions during which potential consumers interact with a serviceprovided by the owner, and such interaction/engagement session lengths722 may be used as an effectiveness measure. Depending on the nature ofthe items and/or services being provided, for example, moreadvertisements may be presented during longer engagement sessions insome embodiments, so the length of the sessions may be tied to revenue.

Optimization Granularities

FIG. 8 illustrates example granularities at which the presentation ofinformational content elements may be optimized, according to at leastsome embodiments. As shown, the granularities 810 may include, amongothers, a global granularity 812, geographical units 814, otherdemographic groups 816 and/or individual granularity 818 in the depictedembodiment.

When optimization is performed at a global granularity 812, the targetaudience to which variants of ICE sets are presented may comprise anypotential item consumers; as such, records of interactions of allconsumers with the different variants being explored may be used tolearn the effectiveness of the ICEs presented. In some cases, thepopularity (and the reasons for the popularity) of at least some itemsmay vary from one geographical region or country to another, and it maybe possible to segregate the records of interactions with the differentICE versions geographically. In some such scenarios, in effect,respective machine learning models may be used for each geographicalregion of interest in various embodiments.

Other demographic groups 816, such as groups based on age, income,gender or the like may be used for separate optimization of ICEpresentation in some embodiments. Finally, in at least one embodiment,the optimizations may be customized to the level of individual consumers818—that is, a personalized optimal set of ICEs to be presented torespective individual consumers may be generated. In variousembodiments, characteristics specific to the group or individual may berepresented, for example, in the feature vectors used in the variantexploration model and/or a model used to identify a baseline set ofICEs. In some embodiments, model parameters learned for one demographicgroup or individual may be transferred or shared (at least as startingpoints in the exploration iterations) with other models for similargroups or individuals.

In various embodiments, the target audiences to which variants of ICEsare presented during optimization iterations may be selected based atleast in part on the optimization granularity level selected. In someembodiments, peer groups of potential consumers may be identified for atleast some potential consumers, so that it becomes easier to collectfeedback when individual-level granularity is employed. That is, in suchembodiments, the effectiveness of a given ICE set with respect to agiven individual may be approximated using the effectiveness of that ICEset with respect to a set of peers identified for that individual. Otheroptimization granularities that those shown in FIG. 8 may be used insome embodiments.

Programmatic Interactions

In various embodiments, an informational content optimization service ortool may implement one or more programmatic interfaces to allow clientsto submit various types of requests, receive response to those requests,and so on. Any of a number of different types of programmatic interfacesmay be implemented in different embodiments, such as a set ofapplication programming interfaces (APIs), web-based consoles, commandline tools, graphical user interfaces and the like.

FIG. 9 illustrates example programmatic interactions between aninformational content optimization service and its clients, according toat least some embodiments. Clients 902 may submit ICE presentationoptimization requests 920 to the service or tool 910 via programmaticinterfaces 977. A given presentation optimization request 920 mayindicate the one or more items 921 for which content presentation is tobe optimized; in some embodiments, a pointer to a database containingrecords of an inventory or a subset of an inventory may be provided inthe request. The request 920 may indicate ICE baseline information 922for the targeted items in various embodiments—e.g., the baseline set ofICEs for one or more presentation contexts may be indicated explicitly,rules/heuristics may be provided, or a directive to use a machinelearning model to identify baseline ICEs may be provided. Informationabout the available variants of ICEs 923 of various categories and/orinstructions on how to obtain ICEs for the items may be included in therequest 920 in some embodiments—e.g., information on how to access adatabase of existing ICEs may be provided, or permission togenerate/synthesize new ICEs may be granted. The interaction recordsources 924 to be used to obtain effectiveness metrics for presentedICEs may be indicated in a request 920 in at least one embodiment. Thepresentation contexts 925 for which optimization is to be performed,and/or one or more optimization granularities 926 may be represented asparameters of request 920 in some embodiments.

In response to receiving such a request 920, the informational contentoptimization service or tool 910 may implement a workflow similar tothat discussed in the context of FIG. 5 for the indicated items 921 invarious embodiments, perform one or more optimization iterations andreturn a set of ICE recommendations 940 to the client 902. In someembodiments, ICE recommendations 940 may be provided iteratively—thatis, in response to a given request 920, the service or tool 910 mayrecommend ICE sets several different times as it learns more about theeffectiveness of the different variants tried.

In at least one embodiment, clients 902 may submit requests 950 to theinformational content optimization service or tool to generate syntheticICEs, e.g., if the set of available ICEs for a particular item is deemedinsufficient by the client. Such a request 950 may, for example, includeparameters indicating the target item 951 for which new ICEs are to beproduced, the set of available ICE variants 952 (if any are available)of one or more ICE categories, and/or targeted ICE characteristics 953(such as, for example, “bright colors” or “unusual font” in the case ofa book cover image). In response to such a request, in some embodimentsa machine learning algorithm such as an algorithm implemented using oneor more generative neural networks may be employed to produce a set ofsynthetic ICEs 954 for the client.

Provider Network Environment

In some embodiments, the techniques discussed above for analyzing andimproving informational content presentation may be implemented at anetwork-accessible service. FIG. 10 illustrates a provider networkenvironment at which an informational content optimization service maybe implemented, according to at least some embodiments. Networks set upby an entity such as a company or a public sector organization toprovide one or more network-accessible services (such as various typesof cloud-based computing, storage or analytics services) accessible viathe Internet and/or other networks to a distributed set of clients maybe termed provider networks in one or more embodiments. A providernetwork may sometimes be referred to as a “public cloud” environment.The resources of a provider network may in some cases be distributedacross multiple data centers, which in turn may be distributed amongnumerous geographical regions (e.g., with each region corresponding toone or more cities, states or countries).

In the depicted embodiment, provider network 1001 may comprise resourcesused to implement a plurality of services, including for example avirtual computing service (VCS) 1003, a database or storage service1023, a machine learning service (MLS) 1071 and an informational contentoptimization service (ICOS) 1043. In some embodiments, the ICOS 1043 maybe implemented as a subcomponent of the MLS 1071. Components of a givenservice may utilize components of other services in the depictedembodiment—e.g., for some machine learning tasks, a component of themachine learning service 1071 may utilize virtual machines implementedat computing platforms such as 1005A-1005D of the virtualized computingservice. Input data, intermediate results, final results and/or otherartifacts of various machine learning algorithms or models, such asthose used for identifying baseline ICE sets, identifying ICErecommendations, or generating synthetic ICEs may be stored at storageservers 1025 (e.g., 1025A-1025D) of the database or storage service 1023in some embodiments. Individual ones of the services shown in FIG. 10may implement a respective set of programmatic interfaces 1077 which canbe used by external and/or internal clients (where the internal clientsmay comprise components of other services) in the depicted embodiment.

As shown, the informational content optimization service 1043 maycomprise, among other components, one or more analysis workflowcoordinators 1047 in the depicted embodiment. The analysis coordinators1047 may, for example, invoke algorithms selected from the machinelearning algorithm library 1075 to train and/or execute one or moremodels required to implement workflows similar to those shown in FIG. 5in the depicted embodiment. In some embodiments, requests to train sometypes of machine learning models (such as some regression, decisiontree-based, or deep neural network models which may be used to identifybaseline ICE sets) may be handled as batch jobs at the machine learningservice, and a batch job scheduler 1079 may orchestrate the allocationof resources for the jobs as well as dependencies among jobs. In thedepicted embodiment, online/real-time analysis managers 1077 of the MLS1071 may be responsible for executing the algorithms (such as banditalgorithms) used to explore the effectiveness of ICE variants. In atleast one embodiment, a machine learning service 1071 may have access toor include a set of execution platforms 1076 that are optimized formachine learning tasks (e.g., platforms that have customized hardwaresuch as GPU arrays and/or customized software stacks). Depending on thesuitability of such platforms for ICE optimization-related tasks, one ormore execution platforms 1076 may be employed for ICE optimization inthe depicted embodiment.

In at least some embodiments, the workflows discussed earlier for ICEpresentation optimization may be accomplished using non-specializedcomputing platforms of the virtualized computing service 1003. Invarious embodiments, the training and test/evaluation data used forvarious models for ICE optimization (and/or records of the iteminventory and the ICE variants) may be stored at a database/storageservice 1023. As mentioned earlier, the techniques for analyzing theeffectiveness of ICEs and identifying recommended ICEs described abovemay be implemented without acquiring resources of network-accessibleservices such as those shown in FIG. 10 in at least some embodiments.For example, a standalone tool implemented at one or more computingdevices which are not part of a network-accessible service may be usedin some embodiments.

Methods for Optimizing Presentation of Informational Content

FIG. 11 is a flow diagram illustrating aspects of operations that may beperformed to optimize the presentation of informational contentelements, according to at least some embodiments. As shown in element1101, a baseline set of informational content elements (ICEs) pertainingto an item of an item collection, to be presented to potential itemconsumers in at least one presentation context, may be identified. Anumber of variant examples of one or more categories of ICEs, such asimages, text snippets and the like, may be available for the item, fromwhich some subset may be selected as members of the baseline set. Insome embodiments, the baseline set may be identified using a firstmachine learning model trained using a training data set whose inputexamples include labeled records of customer interactions and featuresof ICEs presented during or prior to the interactions (e.g., with thelabels indicating effectiveness results of previously-presented sets ofcontent elements). In other embodiments, machine learning may not beused in selecting the baseline set of ICEs; instead, heuristics or rules(such as rules that result in the inclusion of the most recent versionsof various categories of ICEs to be included in the baseline) may beused, or random selection may be used.

One or more iterations of variant exploration and optimization withrespect to the ICEs presented may be initiated in the depictedembodiment. A set of variants of the (current) baseline ICEs may beidentified in a given iteration (element 1104) for a given presentationcontext and a given target audience. For example, different versions ofbook cover images may be included in respective variant ICE sets in oneembodiment, to be tried out in recommendation contexts for a selecteddemographic group of potential buyers/readers of the book. In oneembodiment, one or more of the ICEs explored may be generated usingcontent generation tools, e.g., instead of being selected frompre-existing ICEs provided by item producers.

The ICE variants may be presented to the target audience over some timeperiod, and a data set indicating interactions of the target audiencewith the different variants may be obtained (element 1107) in someembodiments. Using the interaction results obtained from the variants asinput to a second machine learning model, the effectiveness of variousindividual ICE variants may be predicted (element 1110). In someembodiments, a bandit model (such as a contextual bandit model) may beexecuted in an online or continuous prediction mode to generate thepredictions. Any of a number of different effectiveness metrics may beused in the depicted embodiment, such as one or more of the metricsdiscussed in the context of FIG. 7. Using the results of the secondmodel, a recommended set of ICEs for the item may be identified for oneor more presentation contexts, and the recommended set may be presentedat least to the target audiences for which the optimization iterationsare being performed (element 1113).

An optimization goal or termination criterion may be identified in someembodiments, indicating the conditions under which the optimizationiterations are to be discontinued. Example criteria may include, amongothers, when the net relative improvements in effectiveness achieved viaone or more previous iterations fails to exceed some threshold, or whenthe absolute number of sales/consumption events for the item in a giventimeframe falls below a threshold. If the termination criterion has notbeen reached (as detected in element 1116), the next optimizationiteration may be initiated (element 1119) with new variants in thedepicted embodiment, repeating operation corresponding to elements 1104onwards. Optionally, the recommended set of ICEs of the previousiteration may be designated as the new baseline in some embodiments. Ifthe termination criterion or criteria have been met (as also detected inoperations corresponding to element 1116), further ICE optimization forthe item being considered may be discontinued (element 1122).

It is noted that in various embodiments, some of the operations shown inFIG. 11 may be implemented in a different order than that shown in thefigure, or may be performed in parallel rather than sequentially.Additionally, some of the operations shown in FIG. 11 may not berequired in one or more implementations.

Use Cases

The techniques described above, of iteratively identifying recommendedinformational content elements for various inventory items andpresentation contexts may be useful in a variety of scenarios. ManyInternet-based retailing/wholesaling organizations, online auctionorganizations, libraries or subscription sites for video and/or textcontent may have very large inventories, running into millions ofobjects. Many different versions of collateral material, such as images,review text excerpts and the like may be available for a given item,especially an item such as a popular classic book with multiplepublished editions. In some cases, it may also be possible to synthesizeor generate some of the collateral information, e.g., based on analyzingthe relative success of presenting pre-existing content elements in thedifferent contexts. Using machine learning-based optimization algorithmssuch as bandit algorithms as described, it may be possible to identifythe right set of informational content elements to help achieve itemconsumption goals of the inventory owners at desired levels ofoptimization granularity—e.g., for different demographic or geographicgroups of potential item consumers.

Illustrative Computer System

In at least some embodiments, a server that implements a portion or allof one or more of the technologies described herein, including thevarious components of an informational content optimization service suchas workflow coordinators, baseline ICE set identifiers, variantexplorers, synthetic content generators, trainers and executors ofmachine learning models, and the like may include a general-purposecomputer system that includes or is configured to access one or morecomputer-accessible media. FIG. 12 illustrates such a general-purposecomputing device 9000. In the illustrated embodiment, computing device9000 includes one or more processors 9010 coupled to a system memory9020 (which may comprise both non-volatile and volatile memory modules)via an input/output (I/O) interface 9030. Computing device 9000 furtherincludes a network interface 9040 coupled to I/O interface 9030.

In various embodiments, computing device 9000 may be a uniprocessorsystem including one processor 9010, or a multiprocessor systemincluding several processors 9010 (e.g., two, four, eight, or anothersuitable number). Processors 9010 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 9010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 9010 may commonly,but not necessarily, implement the same ISA. In some implementations,graphics processing units (GPUs) may be used instead of, or in additionto, conventional processors.

System memory 9020 may be configured to store instructions and dataaccessible by processor(s) 9010. In at least some embodiments, thesystem memory 9020 may comprise both volatile and non-volatile portions;in other embodiments, only volatile memory may be used. In variousembodiments, the volatile portion of system memory 9020 may beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM or any other type ofmemory. For the non-volatile portion of system memory (which maycomprise one or more NVDIMMs, for example), in some embodimentsflash-based memory devices, including NAND-flash devices, may be used.In at least some embodiments, the non-volatile portion of the systemmemory may include a power source, such as a supercapacitor or otherpower storage device (e.g., a battery). In various embodiments,memristor based resistive random access memory (ReRAM),three-dimensional NAND technologies, Ferroelectric RAM, magnetoresistiveRAM (MRAM), or any of various types of phase change memory (PCM) may beused at least for the non-volatile portion of system memory. In theillustrated embodiment, program instructions and data implementing oneor more desired functions, such as those methods, techniques, and datadescribed above, are shown stored within system memory 9020 as code 9025and data 9026.

In one embodiment, I/O interface 9030 may be configured to coordinateI/O traffic between processor 9010, system memory 9020, and anyperipheral devices in the device, including network interface 9040 orother peripheral interfaces such as various types of persistent and/orvolatile storage devices. In some embodiments, I/O interface 9030 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., system memory 9020) intoa format suitable for use by another component (e.g., processor 9010).In some embodiments, I/O interface 9030 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 9030 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 9030, such as an interface to system memory 9020, may beincorporated directly into processor 9010.

Network interface 9040 may be configured to allow data to be exchangedbetween computing device 9000 and other devices 9060 attached to anetwork or networks 9050, such as other computer systems or devices asillustrated in FIG. 1 through FIG. 11, for example. In variousembodiments, network interface 9040 may support communication via anysuitable wired or wireless general data networks, such as types ofEthernet network, for example. Additionally, network interface 9040 maysupport communication via telecommunications/telephony networks such asanalog voice networks or digital fiber communications networks, viastorage area networks such as Fibre Channel SANs, or via any othersuitable type of network and/or protocol.

In some embodiments, system memory 9020 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above for FIG. 1 through FIG. 11 for implementingembodiments of the corresponding methods and apparatus. However, inother embodiments, program instructions and/or data may be received,sent or stored upon different types of computer-accessible media.Generally speaking, a computer-accessible medium may includenon-transitory storage media or memory media such as magnetic or opticalmedia, e.g., disk or DVD/CD coupled to computing device 9000 via I/Ointerface 9030. A non-transitory computer-accessible storage medium mayalso include any volatile or non-volatile media such as RAM (e.g. SDRAM,DDR SDRAM, RDRAM, SRAM, etc.), ROM, etc., that may be included in someembodiments of computing device 9000 as system memory 9020 or anothertype of memory. Further, a computer-accessible medium may includetransmission media or signals such as electrical, electromagnetic, ordigital signals, conveyed via a communication medium such as a networkand/or a wireless link, such as may be implemented via network interface9040. Portions or all of multiple computing devices such as thatillustrated in FIG. 12 may be used to implement the describedfunctionality in various embodiments; for example, software componentsrunning on a variety of different devices and servers may collaborate toprovide the functionality. In some embodiments, portions of thedescribed functionality may be implemented using storage devices,network devices, or special-purpose computer systems, in addition to orinstead of being implemented using general-purpose computer systems. Theterm “computing device”, as used herein, refers to at least all thesetypes of devices, and is not limited to these types of devices.

CONCLUSION

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. The orderof method may be changed, and various elements may be added, reordered,combined, omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description to be regarded in an illustrative rather than arestrictive sense.

What is claimed is:
 1. A system, comprising: one or more computingdevices of an artificial intelligence service for optimizingpresentation of informational content; wherein the one or more computingdevices are configured to: train, using a first data set comprisingrecords of interactions of item consumers with presentations ofinformational content elements pertaining to a plurality of items of aninventory of items accessible via a network, a first machine learningmodel to generate respective effectiveness scores of a plurality ofinformational content element features in one or more presentationcontexts including at least a recommendation context, wherein theplurality of informational content element features comprise one or moreimage-based features; identify, using a trained version of the firstmachine learning model, a baseline set of informational content elementspertaining to a particular item, wherein the baseline set is to bepresented to one or more potential item consumers in a particularpresentation context, wherein the baseline set comprises a first versionof a first informational content element selected from among a pluralityof versions of the first informational content element; and perform oneor more optimization iterations, wherein a first optimization iterationof the one or more optimization iterations comprises: obtaining anadditional data set comprising records of interactions, in at least theparticular presentation context, of a first target audience with (a) thebaseline set and (b) one or more variants of the baseline set, wherein aparticular variant of the one or more variants comprises a secondversion of the first informational content element; determining, usingthe respective additional data set as input to a second machine learningmodel operating in an online mode, a respective effectiveness metriccorresponding to individual informational content elements with respectto the particular presentation context; and causing a particular set ofinformational content elements to be presented to one or more potentialitem consumers in the particular presentation context, wherein at leastone member of the particular set is identified using the second machinelearning model.
 2. The system as recited in claim 1, wherein the one ormore computing devices are configured to: generate, using a thirdmachine learning model, at least a particular version of aninformational content element included within one or more of: (a) thebaseline set or (b) the one or more variants, wherein the third machinelearning model comprises a generative neural network.
 3. The system asrecited in claim 1, wherein the second machine learning model comprisesone or more of: a contextual bandit model, or a reinforcement learningmodel.
 4. The system as recited in claim 1, wherein the first machinelearning model comprises one or more of: a neural network model, adecision tree-based model, or a regression model.
 5. The system asrecited in claim 1, wherein the one or more computing devices areconfigured to: select, from a plurality of optimization granularitiescomprising an individual-level granularity, a group granularity, and aglobal granularity, a particular optimization granularity at whichpresentation of informational content items is to be optimized for oneor more items including the particular item, wherein the first targetaudience is selected based at least in part on the particularoptimization granularity.
 6. A method, comprising: performing, by one ormore computing devices: identifying a baseline set of informationalcontent elements pertaining to a particular item of an inventory ofitems accessible via a network, wherein the baseline set is to bepresented to one or more potential item consumers in a particularpresentation context, and wherein the baseline set comprises a firstversion of a first informational content element selected from among aplurality of versions of the first informational content element; andimplementing one or more optimization iterations, wherein a firstoptimization iteration of the one or more optimization iterationscomprises: obtaining a first data set comprising records ofinteractions, in at least the particular presentation context, of afirst target audience with (a) the baseline set and (b) one or morevariants of the baseline set, wherein a particular variant of the one ormore variants comprises a second version of the first informationalcontent element; determining, using the first data set as input to afirst machine learning model, a respective effectiveness metriccorresponding to individual informational content elements with respectto the particular presentation context; and causing a particular set ofinformational content elements to be presented to one or more potentialitem consumers in the particular presentation context, wherein at leastone member of the particular set is identified using results of thetrained version of the first machine learning model.
 7. The method asrecited in claim 6, wherein the first machine learning model comprisesone or more of: a contextual bandit model, or a reinforcement learningmodel.
 8. The method as recited in claim 6, wherein said identifying thebaseline set comprises utilizing a second machine learning model trainedusing records of interactions of potential customers with presentationscomprising informational content elements.
 9. The method as recited inclaim 6, wherein the particular presentation context comprises one ormore of: a recommendation context, a search context, an item comparisoncontext, an item overview context, or an item details context.
 10. Themethod as recited in claim 6, wherein a first member of the particularset identified using results of the trained version of the first machinelearning model comprises one or more of: (a) an image, (b) a collectionof text, (c) a video, or (d) an audio recording.
 11. The method asrecited in claim 6, wherein the effectiveness metric comprises one ormore of: (a) a web link click metric, (b) a sales metric, (c) a cartinsertion metric, (d) a wish list insertion metric, or (e) a sessionengagement length metric.
 12. The method as recited in claim 6, furthercomprising performing, by the one or more computing devices: generatingone or more synthetic informational content elements; and including,within a particular variant of the baseline set, at least one syntheticinformational content element of the one or more synthetic informationalcontent elements.
 13. The method as recited in claim 12, wherein saidgenerating the one or more synthetic content elements comprisesutilizing a machine learning model which includes a generative neuralnetwork.
 14. The method as recited in claim 6, further comprisingperforming, by the one or more computing devices: determining, from aplurality of optimization granularities comprising an individual-levelgranularity, a group granularity, and a global granularity, a particularoptimization granularity at which presentation of informational contentitems is to be optimized for one or more items, wherein the first targetaudience is selected based at least in part on the particularoptimization granularity.
 15. The method as recited in claim 6, furthercomprising performing, by the one or more computing devices: selectingthe particular presentation context from a plurality of presentationcontexts based at least in part on an analysis of records ofinteractions in one or more of the plurality of presentation contexts.16. A non-transitory computer-accessible storage medium storing programinstructions that when executed on one or more processors cause the oneor more processors to: generate, using a machine learning-based contentgenerator, one or more synthetic informational content elementspertaining to a particular item of a plurality of items of an inventory,wherein the one or more synthetic informational content elementscomprise at least a first image; obtain a first data set comprisingrecords of interactions of a first target audience with (a) a baselineset of informational content elements pertaining to the particular itemand (b) one or more variants of the baseline set, wherein the baselineset does not include the first image, and wherein a particular variantof the one or more variants includes the first image; determine, using afirst machine learning model to which the first data set is provided asinput, a respective effectiveness metric corresponding to one or moreinformational content elements; and cause a particular set ofinformational content elements to be presented to one or more potentialitem consumers, wherein at least one member of the particular set isidentified using results of the trained version of the first machinelearning model.
 17. The non-transitory computer-accessible storagemedium as recited in claim 16, wherein the first machine learning modelcomprises one or more of: a contextual bandit model, or a reinforcementlearning model.
 18. The non-transitory computer-accessible storagemedium as recited in claim 16, wherein the instructions when executed onthe one or more processors cause the one or more processors to: utilizea second machine learning model to obtain the baseline set.
 19. Thenon-transitory computer-accessible storage medium as recited in claim16, wherein the instructions when executed on the one or more processorscause the one or more processors to: determine a particular presentationcontext from which the first data set is to be obtained, wherein theparticular presentation context comprises one or more of: arecommendation context, a search context, an item comparison context, anitem overview context, or an item details context.
 20. Thenon-transitory computer-accessible storage medium as recited in claim16, wherein a first member of the particular set identified usingresults of the trained version of the first machine learning modelcomprises one or more of: (a) an image, (b) a collection of text, (c) avideo, or (d) an audio recording, and wherein the first member isgenerated using the machine learning-based content generator.
 21. Thenon-transitory computer-accessible storage medium as recited in claim16, wherein the instructions when executed on the one or more processorscause the one or more processors to: generate, based at least in part onthe records of the interactions, one or more additional syntheticcontent information elements at the machine learning-based contentgenerator.